CAAI Transactions on Intelligence Technology最新文献

{"title":"Task-Aligned Haze Removal With Semantic-Aware Fusion and Contrast Self-Correction","authors":"Jinbin Wang,&nbsp;Aiping Yang,&nbsp;Guosong Jiang,&nbsp;Wenlong Yu,&nbsp;Dongwei Ren,&nbsp;Qinghua Hu","doi":"10.1049/cit2.70162","DOIUrl":"https://doi.org/10.1049/cit2.70162","url":null,"abstract":"<p>Adverse haze conditions introduce complex degradations that obscure scene details and distort structural cues critical for object detection, posing persistent challenges for vision-based sensing systems. Although existing haze removal methods have achieved notable improvements in visual clarity, their optimisation objectives are often misaligned with downstream detection requirements, leading to limited detection performance in real-world scenarios. To address this issue, this work proposes a task-aligned weakly supervised haze removal framework, termed Dehaze4Detection, which explicitly aligns low-level restoration with high-level detection objectives. The framework incorporates a Semantic-Aware Multi-Scale Fusion Module (SMFM) that embeds pixel-level semantic knowledge into the dehazing process, enabling selective enhancement of object regions while suppressing background over-enhancement to achieve detection-friendly restoration. To further improve feature quality, a Contrast Self-Correction Attention Module (CSAM) is introduced, which exploits contrast maps derived from pseudo-dehazed images to guide attention towards object contours and salient structures. The proposed SMFM and CSAM are integrated into a unified pipeline and jointly optimised using detection and dehazing losses. Extensive experiments on both synthetic and real-world benchmarks demonstrate that Dehaze4Detection achieves competitive perceptual quality while significantly improving detection accuracy.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"11 4","pages":"978-993"},"PeriodicalIF":5.1,"publicationDate":"2026-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.70162","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148859985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Novel Full-Chain Loop Tracking Auditing Framework for Obtaining Audit Evidence With Reasonable Assurance","authors":"Xiaojia Wang,&nbsp;Ziqing Luo,&nbsp;Chaoxu Mu","doi":"10.1049/cit2.70154","DOIUrl":"https://doi.org/10.1049/cit2.70154","url":null,"abstract":"<p>As an attestation engagement, auditing is required to provide reasonable assurance for its conclusions. Traditional auditing has limited capacity to handle unstructured data and is usually based on audit sampling techniques, which can lead to the neglect of important audit evidence during the auditing process and result in a higher audit risk, making the assurance level uncontrollably guaranteed. Meanwhile, the nature of attestation risk, which is characterised by multi-factor triggering chain transmission and time evolution, brings difficulties to the acquisition of critical audit evidence. Therefore, this paper realizes the Full-chain Loop Tracking Audit through the business cycle audit process of ‘Obtaining Evidence of Internal Sequential Timing Audit—Obtaining Evidence of Horizontal Expansion of Audit Matters—Penetrating the Multi-Dimensional Evidence Association Rules—Achieving the Anchor Point at The Reasonable Assurance Level’. This is called the Full-chain Loop Tracking Auditing framework (FcLTA framework). Firstly, ‘Obtaining Evidence of Internal Sequential Timing Audit’ is a process of identifying and obtaining abnormal trends in accounting book facts. This process obtains ‘Time Series Audit Evidence’ by embedding the time characteristics of audit evidence in the risk feature vector within the table. Secondly, ‘Obtaining Evidence of Horizontal Expansion of Audit Matters' is a ‘Triangular Verification’, system constructed by integrating financial, business and public opinion data. This process provides ‘Horizontal Expansion Audit Evidence’ by identifying isolated risk signals in financial information to address issues such as discrepancies between accounts and reality and performance whitewashing. Thirdly, ‘Penetrating the Multi-Dimensional Evidence Association Rules' is a process of integrating audit evidence and resolving conflicts. This process generates ‘Multi-Dimensional Penetration Audit Evidence Chain’ by constructing a high-order association across modalities and dimensions for verifying risks. Finally, ‘Achieving the Anchor Point at The Reasonable Assurance Level’ is a testing process for detecting risks with the goal of obtaining high-level assurance of audit evidence. It mainly includes two processes: predicting the level of audit assurance and determining whether to continue the audit. This paper has implemented the full-chain circular tracking audit through the FcLTA framework, and the generated audit evidence can meet the high-level assurance requirements of the attestation engagements, reducing the risk of audit failure.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"11 4","pages":"1194-1214"},"PeriodicalIF":5.1,"publicationDate":"2026-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.70154","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148860186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cyberattack-Resilient Fractional-Order PPI Control for Frequency Regulation in PV-Integrated Power Systems","authors":"Bora Çavdar,&nbsp;Erdinç Şahin,&nbsp;Fatih Mehmet Nuroğlu","doi":"10.1049/cit2.70156","DOIUrl":"https://doi.org/10.1049/cit2.70156","url":null,"abstract":"<p>Load-frequency control (LFC) comprises a primary process in interconnected electrical power systems, playing a critical role in maintaining the stability and reliability of the electrical grid. It is of paramount importance that the designed controller functions in an optimal manner, particularly with regard to the compression of area frequency and tie-line power deviations within the scheduled limits. In light of these considerations, this study puts forth the inaugural derivative-free and delay-based application of a predictive proportional-integral (PPI) and its fractional-order approach (FOPPI) controllers to address the LFC challenge in a PV-integrated two-area power system. To attain the most effective controller parameters of the PPI and FOPPI, the artificial rabbits optimisation (ARO) methodology is utilised by aiming to minimise the integral of the time-weighted absolute error (ITAE) performance measurement. The effectiveness of the proposed FOPPI controller is evaluated through a comparative analysis of existing controller structures in the literature and the classical PPI controller based on reported ITAE values in the literature. The proposed approach is validated through a series of detailed and systematic performance evaluations, such as time-domain analysis, uncertainty in system parameters, stochastic loading conditions, stability analysis and cyberattack performance of the proposed controllers. Furthermore, a cyberattack detection index for the controllers is considered together with a statistical analysis, and the dataset and simulation models supporting the study are made available as described in the Data Availability Statement. As evidenced by the outcomes of the performance assessments, the FOPPI controller, as conceptualised in this investigation, displays an augmented capacity for dynamic response and resilience in the face of specified scenarios when contrasted with alternative methodologies, including the PPI controller.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"11 4","pages":"1062-1087"},"PeriodicalIF":5.1,"publicationDate":"2026-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.70156","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148860187","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Convergent Interval Confirmation for Three-Step Discrete Specific Zeroing Neural Dynamics Illustrated With Repetitive Motion Planning of Redundant Manipulators","authors":"Ying Kong,&nbsp;Shaoyuan Sun,&nbsp;Yunliang Jiang,&nbsp;Lin Wei","doi":"10.1049/cit2.70126","DOIUrl":"https://doi.org/10.1049/cit2.70126","url":null,"abstract":"<p>Repetitive motion planning (RMP) for redundant manipulators with high convergent precision becomes an intense research topic due to its more degrees of freedom. In this paper, a specific zeroing neural dynamics (SZND) model for the RMP is first set up via zeroing neurodynamics. After that, a general linear three-step rule is presented and applied to discretise the SZND model. Then, we obtain a three-step discrete SZND (TS-DSZND) model for repetitive kinematics. It is necessary to point out that the stepsize in TS-DSZND algorithm is intimated to the stability of the proposed TS-DSZND. By utilizing linear transformation and the Jury stability criterion, the optimal stepsize interval of the TS-DSZND is obtained by a theorem. Furthermore, the truncation error constant, which tends to be neglected with most of the ZeaD-typed discretisation formulas for the RMP of redundant manipulators, is analysed and exhibits the impact for the convergent accuracy compared with the corresponding three-step rules. Besides, simulative illustrations on various redundant manipulators show the superiority and feasibility of the proposed TS-DSZND as well as the effective stepsize domain. Finally, a physical experiment on a real UR5 robotic arm for fulfiling a flower-shaped path validates the satisfying performance of the TS-DSZND model.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"11 4","pages":"1274-1283"},"PeriodicalIF":5.1,"publicationDate":"2026-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.70126","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148859981","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Boosting Embodied Visual Localisation Through Multi-Granular Semantics and Multi-Robot Consensus","authors":"Wenshuai Wang,&nbsp;Shiyu Liu,&nbsp;Peifeng Jiang,&nbsp;Hong Liu,&nbsp;Runwei Ding","doi":"10.1049/cit2.70152","DOIUrl":"https://doi.org/10.1049/cit2.70152","url":null,"abstract":"<p>Achieving high accuracy and synergy remains extremely difficult for multi-robot embodied visual localisation, which suffers from persistent real-world challenges such as viewpoint ambiguity, appearance variation and dynamic occlusion. Conventional optimisation-based methods often lead to incorrect feature matching without domain adaptation, whereas existing deep learning models are limited in both precision and generalisability for pose regression. Although semantic information can offer a cognitive-level scene representation, its potential has not been fully exploited due to inconsistent labelling and scaling. To address these issues, this paper proposes a novel semantic and synergic consensus framework (termed SSC-MR) that enhances image retrieval and pose estimation by leveraging multi-granular semantics and multi-robot consensus. Specifically, the spatial topology is attentively generated as a global scene graph for fast image retrieval across heterogeneous robots in a holistic workspace, whereas a local semantic-guided pose estimation strategy refines matched keypoints under semantic consistency constraints. To further improve the efficiency of multi-robot collaboration, an intra-robot minimum redundancy consensus module selects informative keyframes and compresses feature representations to reduce perceptual redundancy, and an inter-robot maximum correlation consensus module further establishes high-confidence state associations among interacting robots for knowledge sharing. Extensive experiments on five public datasets demonstrate that the proposed SSC-MR obtains superior effectiveness and robustness across diverse indoor and outdoor scenes, providing a reliable and scalable solution for embodied visual localisation in practical robotic applications. Notably, it achieves a 62.1% improvement in localisation accuracy with nearly 22× faster speed compared to a typical distributed architecture for 10 collaborative robots.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"11 4","pages":"1088-1103"},"PeriodicalIF":5.1,"publicationDate":"2026-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.70152","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148860104","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploration and Exploitation: A Study on Sample Efficiency in Reinforcement Learning With Multifaceted Curiosity Rewards and Adaptive Experience Replay Utilisation in Sparse Reward Environments","authors":"Jingyi Huang,&nbsp;Guipeng Xi,&nbsp;Meixiu Lin,&nbsp;Haohui Zhang,&nbsp;Bo Li,&nbsp;Evgeny Neretin","doi":"10.1049/cit2.70153","DOIUrl":"https://doi.org/10.1049/cit2.70153","url":null,"abstract":"<p>In recent years, the widespread application of deep reinforcement learning (DRL) in autonomous systems has highlighted the importance of achieving high sample efficiency under sparse reward conditions. To improve sample efficiency in sparse reward environments, this paper proposes a reinforcement learning framework built upon the Soft Actor Critic architecture, which integrates multifaceted curiosity rewards (MCR) and adaptive experience replay utilisation (AERU) (MCR-AERU SAC). MCR combines multi-level intrinsic motivational signals, such as state prediction error and model uncertainty, to provide rich exploration incentives, encouraging the controlled entity to deviate from existing trajectories and discover new high-reward behaviours. AERU dynamically adjusts the experience replay priorities based on posterior temporal difference error (TD error), focusing on utilising ‘partially successful’ transitions that are easily overlooked. The synergy between MCR and AERU enables the proposed framework to achieve an optimal balance between exploration and exploitation, significantly accelerating policy convergence and improving sample utilisation. Extensive experiments in complex dynamic training environments demonstrate that the proposed MCR-AERU SAC algorithm achieves up to 1.41 times the early-stage gain rate and a 55.56% improvement in task success rate compared to the HER-SAC baseline, demonstrating superior sample efficiency and excellent robustness in large-scale sparse reward environments.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"11 4","pages":"1028-1046"},"PeriodicalIF":5.1,"publicationDate":"2026-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.70153","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148860178","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Align What Truly Matters: Pedestrian-Relevant Hierarchical Parsing Network for Text-Based Person Retrieval","authors":"Jiajun Su,&nbsp;Yifan Shi,&nbsp;Pudu Liu,&nbsp;Jianqing Zhu,&nbsp;Huanqiang Zeng,&nbsp;Liu Liu,&nbsp;Zhen Lei","doi":"10.1049/cit2.70163","DOIUrl":"https://doi.org/10.1049/cit2.70163","url":null,"abstract":"<p>Fine-grained alignment is crucial for text-based person retrieval, which searches for relevant pedestrian images using a text query. However, background clutter and semantically vacuous words can cause interference and misalignment, hindering retrieval performance. Existing methods employ masked language modelling to randomly mask and predict words, or leverage multimodal large language models to generate diverse and pure samples. However, these methods rely on external tools and are not sufficiently stable or controllable. In this paper, we propose a pedestrian-relevant hierarchical parsing (PHP) module to extract well-aligned fine-grained visual and textual features for alignment. First, we design a coarse relevant feature mapping (CRFM) module, which uses learnable unified tokens to project both modalities into a shared low-dimensional space, enabling coarse-level semantic filtering. Next, we design an expert-driven feature parsing (EFP) module, which integrates the representational power of a mixture of experts with a modality-aware gating mechanism to uncover deep semantic associations between text and image features. Extensive experiments on three public large-scale datasets demonstrate that our approach outperforms existing state-of-the-art methods, achieving, in particular, a 5.68% improvement in mAP and a 4.81% boost in R1 accuracy on the CUHK-PEDES dataset.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"11 4","pages":"1215-1230"},"PeriodicalIF":5.1,"publicationDate":"2026-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.70163","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148859978","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TMSA-Net: Transformer-Based Multi-Scale Attention U-Net for Flood Image Segmentation","authors":"Parham Imanzadeh Charandabi,&nbsp;Shivakumara Palaiahnakote,&nbsp;Apostolos Antonacopoulos,&nbsp;Christian Clausner","doi":"10.1049/cit2.70169","DOIUrl":"https://doi.org/10.1049/cit2.70169","url":null,"abstract":"<p>Flood detection is essential for real-time applications, including disaster management, emergency response, and alerting people in flood zones. For successful flood detection, accurate flood region segmentation is essential. However, the flood region segmentation is challenging due to the complex background and occlusions with debris and the effect of external adverse factors. In contrast to existing models that focus on satellite and remote sensing images, for real-time applications, this study proposes a new Transformer-based Multi-Scale-Attention U-Net (TMSA-Net) model for flood region segmentation in images with cluttered backgrounds. The proposed model extracts features hierarchically using a Convolutional Neural Network (CNN) encoder. The deepest features are fed to an adapted vision transformer for extracting global context. In the decoder, Multi-Scale Attention Gates are used in skip connections to selectively filter encoder features before fusion. The proposed model is compared with state-of-the-art methods, including Swin-UNet, DeepLabV3+, TransUNet, UNet++, and Attention U-Net. Experimental results show that TMSA-Net achieves a test IoU of 92.01%, with improvements of 2.82% and 5.95% IoU over DeepLabV3+ and TransUNet, respectively, demonstrating the effectiveness of the proposed approach. The newly created experimental dataset, with ground truth at the pixel level, will be publicly available (released upon acceptance).</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"11 4","pages":"1104-1126"},"PeriodicalIF":5.1,"publicationDate":"2026-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.70169","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148859952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Quantised Push-Sum Distributed Adaptive Momentum Algorithm for Optimisation Over Directed Networks","authors":"Qingguo Lü,&nbsp;Mingyu Guan,&nbsp;Chenglong He,&nbsp;Chen Chen,&nbsp;Xiangguang Dai,&nbsp;Man-Fai Leung,&nbsp;Wei Zhang","doi":"10.1049/cit2.70122","DOIUrl":"https://doi.org/10.1049/cit2.70122","url":null,"abstract":"<p>In this paper, we investigate a distributed constrained optimisation problem over directed networks. The agents in the networks conduct local computations and communications, endeavouring to collaboratively minimise the aggregation of all locally known convex cost functions subject to a global constraint set. However, since the agents are constantly transmitting information, most existing algorithms for this problem are prone to communication burdens, especially in large-scale networks under a limited communication bandwidth. Problems of this nature emerge in a number of applications, mostly evident in distributed classification tasks, distributed image restoration, distributed compressive sensing etc. To solve these kinds of problems, we propose an effective quantised push-sum distributed adaptive momentum (QPS-DADAM) algorithm. On the one hand, the QPS-DADAM algorithm employs the random quantiser to reduce the communication overhead and avoid the channel blockage. On the other hand, the QPS-DADAM algorithm incorporates the adaptive momentum method into the push-sum protocol to further accelerate the convergence over directed networks. Rigorous theoretical analyses are provided to illustrate that the QPS-DADAM algorithm converges sublinearly to the optimal solution. In addition, numerical simulations further demonstrate the efficacy of the QPS-DADAM algorithm and the correctness of the theoretical discoveries.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"11 4","pages":"1284-1301"},"PeriodicalIF":5.1,"publicationDate":"2026-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.70122","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148859969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of Intelligent Systems in CO2 Management: State of the Art and Future Prospects","authors":"Muhammad Zulkefal,&nbsp;Iftikhar Ahmad,&nbsp;Asad Habib,&nbsp;Asad Ayub,&nbsp;Hamza Sethi,&nbsp;Ihsanullah Ihsanullah,&nbsp;Hakan Caliskan,&nbsp;Hiki Hong","doi":"10.1049/cit2.70103","DOIUrl":"https://doi.org/10.1049/cit2.70103","url":null,"abstract":"<p>Machine learning (ML) integration is becoming increasingly popular in advancing the 4th industrial revolution, known as Industry 4.0. This review paper examines ML applications in CO₂ management stages: emission, capture, and conversion. ML models, including multiple linear regression (MLR), multiple nonlinear regression (MNLR), and artificial neural networks (ANN), are used in the emission phase to predict the CO₂ emissions for the countries and sectors, variables such as energy consumption, fuel usage, gross domestic product (GDP), population, and other miscellaneous socio-economic variables. In the capture stage, ML forecasts post-combustion and direct air CO₂ capture efficiencies using models such as ANN, generalized regression neural networks (GRNN), backpropagation neural networks (BPNN), and group method of data handling (GMDH), considering input variables such as catalysts, process parameters, and rheological properties. In the conversion stage, ML models, primarily ANN, multi-layer perceptron (MLP), particle swarm optimization (PSO), and genetic algorithm (GA), forecast and optimize CO₂ transformation into products such as methanol, C₂ hydrocarbons, syngas, methane, lower olefins, and biofuels, considering factors such as catalyst compositions and process conditions. This study identifies several trends, with ANN emerging as the predominant ML technique. The discoveries will benefit academia and industry in the development of CO₂ management models.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"11 4","pages":"951-977"},"PeriodicalIF":5.1,"publicationDate":"2026-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.70103","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148860099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}